It is difficult to automatically detect tumors and extract lesion boundaries in ultrasound images due to the variance in shape, the interference from speckle noise, and the low contrast between objects and background. The enhancement of ultrasonic image becomes a significant task before performing lesion classification, which was usually done with manual delineation of the tumor boundaries in the previous works. In this study, a linear support vector machine (SVM) based algorithm is proposed for ultrasound breast image training and classification. Then a disk expansion algorithm is applied for automatically detecting lesions boundary. A set of sub-images including smooth and irregular boundaries in tumor objects and those in speckle-noised background are trained by the SVM algorithm to produce an optimal classification function. Based on this classification model, each pixel within an ultrasound image is classified into either object or background oriented pixel. This enhanced binary image can highlight the object and suppress the speckle noise; and it can be regarded as degraded paint character (DPC) image containing closure noise, which is well known in perceptual organization of psychology. An effective scheme of removing closure noise using iterative disk expansion method has been successfully demonstrated in our previous works. The boundary detection of ultrasonic breast lesions can be further equivalent to the removal of speckle noise. By applying the disk expansion method to the binary image, we can obtain a significant radius-based image where the radius for each pixel represents the corresponding disk covering the specific object information. Finally, a signal transmission process is used for searching the complete breast lesion region and thus the desired lesion boundary can be effectively and automatically determined. Our algorithm can be performed iteratively until all desired objects are detected. Simulations and clinical images were introduced to evaluate the performance of our approach. Several types of cysts with different contours and contrast resolutions images were simulated with speckle characteristics. Four thousand sub-images of tumor objects and speckle-noised background were used for SVM training. Comparison with conventional algorithms such as active contouring, the proposed algorithm does not need to position any initial seed point within the lesion and is able to detect simultaneously multiple irregular shape lesions in a single image, thus it can be regarded as a fully automatic process. The results show that the mean normalized true positive area overlap between true contour and contour obtained by the proposed approach is 90%.

Date Published: 12 March 2007
PDF: 8 pages
Proc. SPIE 6513, Medical Imaging 2007: Ultrasonic Imaging and Signal Processing, 651317 (12 March 2007); doi: 10.1117/12.702960

Published in SPIE Proceedings Vol. 6513:
Medical Imaging 2007: Ultrasonic Imaging and Signal Processing
Stanislav Y. Emelianov; Stephen A. McAleavey, Editor(s)